Imagine a near future where AI agents collaborate seamlessly with you and your colleagues. Some represent customers, others teammates — all communicating in the particular language of your business, even across multiple languages. These agents continuously update a shared knowledge base, integrating both established information and new insight in real time.
As we move from single-agent use cases to multiagent systems and eventually towards every business having its own omniagent, we will see AI agents operate across contexts with broader and broader autonomy. We will start our days with questions like: “What new issues have customers raised this week?” or “What are my employees most concerned about?” An ensemble of agents will summarize the answer from thousands of customer interactions, conversations, and insights from your colleagues. This is the promise of the agentic era.
The question, is how do we get there? The answer involves the kind of information agents need most to execute their tasks effectively: context.
Why context is essential in AI
Context is key for agentic AI to provide real value to companies. Agentic architectures harness the inference power of LLMs to automate tasks despite ambiguity. The more context they have to interpret that ambiguity, the better they perform.
Agents introduce a new way of using computers to automate, one that does not require exhaustively predefining rules for every scenario. Agentic systems can use an LLM’s pattern-matching abilities to infer the particulars of any given situation, and they become more effective as they receive richer context — in other words, the less they have to guess.
The promise of human–AI collaboration is lost when agents must constantly ask for details before acting.
Consider how most early AI agents operate today, versus where we are headed. Many current AI agents struggle outside their defined parameters and can easily misinterpret requests if they lose the thread. If they wait for the user to tell them what to interpret, they force the user to become prompt engineers.
An example of a vague user prompt which requires further prompting to make up for the lack of context.
Without access to rich enough context, agent developers get stuck in a dilemma that defeats the purpose of agents in the first place. They can design narrow use cases with predictable contextual clues and reliable performance but limited flexibility, or they can take a broader approach and design to live without context. But doing so places the burden on users to supply missing details at the last moment. The promise of human–AI collaboration is lost when agents must constantly ask for details before acting. Both strategies can make incremental gains, but they can preclude the adoption of their agents at scale.
When an agent lacks context, users are forced to spend more time clarifying their prompt than completing the actual task. The solution is to launch agents in an environment where they can observe ongoing conversations about work being done and use their interpretation ability to adapt their instructions and tools to the context surrounding real-world work.
Conversations offer the richest and most dynamic source of context in the natural language agents’ LLMs can easily decipher. They contain real-time intent, nuance about what is and is not helpful, and clues about evolving needs — providing the critical cues AI agents need to finesse tradeoffs. Conversational substrates allow AI agents to adapt, become proactive, and act with precision that would be impossible to achieve otherwise.
An example of a Slack Agent using conversational context to interpret the same vague user prompt, and respond effectively.
With context, agents can have the agency that makes them so powerful. They need an environment where they can observe enough context to discern what matters, what doesfin’t, when to proceed, when to seek help, and even when they themselves are unnecessary. If they can see the shifting situation, constraints, and latest information, agentic adoption at scale becomes possible.
Agents need an environment where they can observe enough context to discern what matters, what doesn’t, when to proceed, when to seek help, and even when they themselves are unnecessary.
How Slack enables contextual AI
Slack gives you everything you need to balance control and agency. By using Slack as a substrate for your agents, your company’s unstructured data—user-generated content, natural-language text, audio, and video—enhance agent reasoning and decision-making for better relevance. This helps agents understand what they can do to help, when they should jump in and when they should sit back.
Slack also lets you maintain control over what data your agents can access. We never train LLMs on customer data; instead, we provide the best environment for you to perform contextual Retrieval-Augmented Generation (RAG) on your own data to provide agents with the information they need, when they need it. This means your agents can deduce on the fly which data to retrieve from your databases based on the context surrounding their task.
With Slack’s conversation history API and data access API, you can create adaptive agents capable of making real-time adjustments based on the conversation as it unfolds. Every new Slack message updates context. Instead of rigidly scripted responses, Slack allows agents to become dynamic, using cues from ongoing and past conversations to be more flexible with their instructions and effective at performing their assigned roles or goals. The power of clear real-time context lies in its capability to help AI understand humans. Slack’s architecture supports this adaptability, letting agent builders use the most up-to-date context, and even further contextualize what the agents are listening to from past interactions or ongoing conversations in other Slack channels entirely.
As multi-agent systems become more common, conversational platforms will be at the heart of agentic adoption and transformation. Slack serves as the enterprise conversational bus needed to enable harmonious communication between agents and humans, making sure responses are aligned and productive. Agents can work autonomously, collaborating to enhance each other’s capabilities in real time, and reinforce themselves or improve the underlying model. Agents become informed collaborators, and with Slack’s context services, agent builders can focus on the automation instead of the complicated last mile or giving in to predefining and limiting their agents. And soon, Slack agents will be able to manage and orchestrate others easily, across all apps connected to Slack, with one standardized substrate that allows each agent to tap back into conversations with and between humans to keep themselves grounded.
Slack is where agents can truly augment humans
As you consider the best place for your developers to build and deploy agents, it’s essential to remember that context is not optional — it’s what makes the difference. Slack provides an environment where agents can grow from basic task executors to adaptive, collaborative partners. Slack is where agent and human interactions come together to build an ever-growing body of enterprise knowledge, constantly updated with every message. Slack’s powerful user context is what makes this possible, and ultimately, for each agent to better understand the humans they serve.
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